The objectives of this research are to propose a proper model for detecting defective machines in auto parts factories to apply the proposed model to the defective product data and to investigate the factors influencing on the machine producing defective products. A Bayesian hidden Markov model (HMM) is adopted. The results show that if the number of defective products increases the machine will be in the hidden bad state. The probability of changing from a hidden bad state to a hidden good state is higher than the probability of changing from a hidden good state to a hidden good state. The highest probability that the machine will be in a good state is at the work process 4 followed by 1 3 and 2 respectively. The highest probability that the machine will be in hidden good state is at the Worker no. 10 followed 9 3 12 7 2 1 6 11 4 8 and 5 respectively and The highest probability that the machine will be in the hidden good state is at the product type 1 followed by 2 and 3 respectively.

The objectives of this research are to propose a proper model for defective products in an autoparts manufacturing industry to apply the proposed model to the defective products data to investigate the factors related to the machines producing defective products and to compare the proposed model with the generalized estimating equations model (GEE). The proposed model is a generalized linear mixed model (GLMM) in which the dependent variables have a Poisson distribution. The parameter are estimated using a Bayesian method. The data were collected from 12 machines 15 days for each machine from an autoparts manufacturing factory. The data collection dates vary depending on the working steps and the product types. The results show that the proposed model fits the data. The factors related to the machines producing defective products are working steps workers and product types. According to the estimates of the factors effects the working step producing the largest number of defective products is the working step 2 followed by the working steps 3 1 and 4 respectively. The worker producing the largest of defective products is the worker 4 followed by the workers 10 5 8 6 2 3 1 7 and 9 respectively. The product type 3 has the largest number of defective products followed by the product types 2 and 1 respectively. If the population-averaged effects are focused the GEE is applied. However the subject-specific is focused the GLMM is applied.

This research aims to develop the Para-rubber floor tiles mixed with ethylene vinyl acetate plastic wastes (EVA plastics) from the factories. The study use Para-rubber STR 20 at 100 phr with various contents of EVA plastics (e.g. 0 5 10 20 40 and 80 phr) and then mixed with chemical substance at constant ratio include zinc oxide at 5 phr stearic acid at 2 phr sulfur at 3 phr mercaptobenzothiazole at 0.5 phr and diphenyl guanidine at 0.2 phr. The samples are ground by two-roll mill and formed by compression molding at 150 degree Celsius with 30x30x0.2 cm of dimension. The properties of the Para-rubber floor tiles are tested under ASTM standard. From the results it is found that the Para-rubber floor tiles mixed with 10 phr of EVA plastics is the suitable ratio for using as the rubber floor tiles and rubber wall tiles in building. This ratio can increase the hardness and wear resistant properties and decrease the density and thermal insulation properties when compare to the Para-rubber floor tiles without EVA plastic.

The purposes of this research are to study and improve the productivity process of washing tables by setting standard time a case study of stainless manufactories. Nowadays the manufacturers don’t know the production capacity. That leads to the problems of manufacturers’ work control and late product delivery to the customers. The study is proposed to improve work procedures by designing operation process chart (OPC) the setting of operation stations Operation process chart time (OPC TIME) and finding the standard time in product manufacture. As for the operation process and the standard time setting the research revealed that the standard time of welding station in assembly process for the frame table sink tap nozzle high neck sink flick and welding station of all assembly parts were the first the second the third and the fourth respectively. The researchers’ suggestions were to add 2 welding machines and 2 workers in assembly process so as to work more efficiently so that it can decrease the total processing time from 855.24 minutes/set to 645.91 minutes/set or 24.47 % reduction.

Development of fruit – confectionery products the leftover from fruit – canning factories in small business industry is the second – year continuous project. The aims were to study the product shelf life and to transfer production process to the community and private sectors. The result of shelf – life study indicated that the pH and water activity of Papaya Cream Guava Cream Pineapple Cream Mixed – flavoured fruit (yee) and Banana toffee were 3.78 3.62 3.20 3.48 3.57 3.92 and 0.97 0.97 0.91 0.60 0.51 0.58 respectively. At an 8-week period no microorganism – borne deterioration of the products was found;moreover they were accepted by the consumers. The target group of as participants as planned was trained for a 3 – day period to produce 5 fruit – confectionery products. The training evaluation revealed that the group considered management on instructors teaching materials and laboratories ‘much’ to ‘most appropriate’; whereas training operation was viewed ‘most appropriate’. At the project end it was found that 29.27% 29.27 and 28.05% of the target group made fruit – confectionery for family consumption and as a gift or a souvenir respectively; 9.76% put their products in stores for sale as a supplemental career; 3.65% transferred their knoeledge to others.